Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells4090
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 5 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 3 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
WSPM is highly overall correlated with NO2High correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 628 (1.8%) missing valuesMissing
PM10 has 440 (1.3%) missing valuesMissing
SO2 has 446 (1.3%) missing valuesMissing
NO2 has 692 (2.0%) missing valuesMissing
CO has 1206 (3.4%) missing valuesMissing
O3 has 506 (1.4%) missing valuesMissing
RAIN is highly skewed (γ1 = 27.3358138)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33673 (96.0%) zerosZeros
WSPM has 623 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-08 05:13:27.581263
Analysis finished2024-03-08 05:14:12.581162
Duration45 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:12.720449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:14:12.974203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:14:13.222942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:14:13.463857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:13.691029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:14:13.958441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:14.232575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:14:14.442372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:14.686900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:14:14.920611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct565
Distinct (%)1.6%
Missing628
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean84.838483
Minimum2
Maximum844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:15.201481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q122
median59
Q3116
95-th percentile260
Maximum844
Range842
Interquartile range (IQR)94

Descriptive statistics

Standard deviation86.225344
Coefficient of variation (CV)1.0163471
Kurtosis5.6189337
Mean84.838483
Median Absolute Deviation (MAD)42
Skewness2.0030207
Sum2921498
Variance7434.8099
MonotonicityNot monotonic
2024-03-08T12:14:15.521209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 693
 
2.0%
10 584
 
1.7%
9 560
 
1.6%
8 555
 
1.6%
11 544
 
1.6%
12 542
 
1.5%
7 493
 
1.4%
13 484
 
1.4%
14 470
 
1.3%
6 428
 
1.2%
Other values (555) 29083
82.9%
(Missing) 628
 
1.8%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 693
2.0%
4 242
 
0.7%
5 343
1.0%
6 428
1.2%
7 493
1.4%
8 555
1.6%
9 560
1.6%
10 584
1.7%
11 544
1.6%
ValueCountFrequency (%)
844 1
< 0.1%
835 1
< 0.1%
809 1
< 0.1%
781 1
< 0.1%
744 1
< 0.1%
687 1
< 0.1%
678 1
< 0.1%
670 1
< 0.1%
667 1
< 0.1%
642 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct649
Distinct (%)1.9%
Missing440
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean108.9911
Minimum2
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:15.833405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q138
median85
Q3149
95-th percentile294
Maximum995
Range993
Interquartile range (IQR)111

Descriptive statistics

Standard deviation95.341177
Coefficient of variation (CV)0.87476115
Kurtosis5.4686332
Mean108.9911
Median Absolute Deviation (MAD)52
Skewness1.8449821
Sum3773707.7
Variance9089.94
MonotonicityNot monotonic
2024-03-08T12:14:16.033312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 333
 
0.9%
18 312
 
0.9%
17 303
 
0.9%
16 299
 
0.9%
21 298
 
0.8%
20 291
 
0.8%
19 285
 
0.8%
14 283
 
0.8%
13 282
 
0.8%
24 281
 
0.8%
Other values (639) 31657
90.3%
(Missing) 440
 
1.3%
ValueCountFrequency (%)
2 11
 
< 0.1%
3 26
 
0.1%
4 12
 
< 0.1%
5 241
0.7%
6 333
0.9%
7 192
0.5%
8 200
0.6%
9 216
0.6%
9.5 1
 
< 0.1%
10 252
0.7%
ValueCountFrequency (%)
995 1
< 0.1%
939 1
< 0.1%
909 1
< 0.1%
907 1
< 0.1%
899 1
< 0.1%
890 1
< 0.1%
888 1
< 0.1%
878 1
< 0.1%
811 1
< 0.1%
800 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct263
Distinct (%)0.8%
Missing446
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean18.689242
Minimum0.5712
Maximum257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:16.252483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.5712
5-th percentile2
Q13
median9
Q323
95-th percentile70
Maximum257
Range256.4288
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.280665
Coefficient of variation (CV)1.2991787
Kurtosis9.051378
Mean18.689242
Median Absolute Deviation (MAD)7
Skewness2.6371905
Sum646984.18
Variance589.55068
MonotonicityNot monotonic
2024-03-08T12:14:16.555155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6289
17.9%
3 2523
 
7.2%
4 1878
 
5.4%
5 1550
 
4.4%
6 1459
 
4.2%
7 1285
 
3.7%
8 1257
 
3.6%
9 1109
 
3.2%
10 987
 
2.8%
12 852
 
2.4%
Other values (253) 15429
44.0%
ValueCountFrequency (%)
0.5712 1
 
< 0.1%
1 159
 
0.5%
1.428 1
 
< 0.1%
1.9992 2
 
< 0.1%
2 6289
17.9%
2.2848 2
 
< 0.1%
2.7 1
 
< 0.1%
2.856 1
 
< 0.1%
3 2523
7.2%
3.1416 1
 
< 0.1%
ValueCountFrequency (%)
257 1
< 0.1%
234 1
< 0.1%
226 1
< 0.1%
224 1
< 0.1%
212 1
< 0.1%
208 1
< 0.1%
205 1
< 0.1%
204 1
< 0.1%
202 1
< 0.1%
199 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct595
Distinct (%)1.7%
Missing692
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean58.097172
Minimum2
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:16.766017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q129
median51
Q380
95-th percentile126
Maximum273
Range271
Interquartile range (IQR)51

Descriptive statistics

Standard deviation36.29774
Coefficient of variation (CV)0.62477637
Kurtosis0.92309031
Mean58.097172
Median Absolute Deviation (MAD)24
Skewness0.97444835
Sum1996916
Variance1317.526
MonotonicityNot monotonic
2024-03-08T12:14:17.066310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 470
 
1.3%
30 470
 
1.3%
32 467
 
1.3%
22 452
 
1.3%
28 447
 
1.3%
40 437
 
1.2%
23 435
 
1.2%
20 434
 
1.2%
38 434
 
1.2%
39 432
 
1.2%
Other values (585) 29894
85.3%
(Missing) 692
 
2.0%
ValueCountFrequency (%)
2 47
0.1%
3 15
 
< 0.1%
3.4901 1
 
< 0.1%
3.6954 1
 
< 0.1%
3.9007 2
 
< 0.1%
4 27
0.1%
4.106 1
 
< 0.1%
4.5166 1
 
< 0.1%
4.9272 1
 
< 0.1%
5 40
0.1%
ValueCountFrequency (%)
273 1
< 0.1%
265 1
< 0.1%
262 1
< 0.1%
257 1
< 0.1%
256 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
241 1
< 0.1%
239 1
< 0.1%
237 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)0.3%
Missing1206
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean1324.3502
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:17.263172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1500
median900
Q31600
95-th percentile3800
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1245.1661
Coefficient of variation (CV)0.94020911
Kurtosis7.9268167
Mean1324.3502
Median Absolute Deviation (MAD)500
Skewness2.4522777
Sum44839849
Variance1550438.7
MonotonicityNot monotonic
2024-03-08T12:14:17.466619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 2615
 
7.5%
400 2478
 
7.1%
500 2208
 
6.3%
700 2198
 
6.3%
600 2155
 
6.1%
800 2003
 
5.7%
900 1757
 
5.0%
1000 1738
 
5.0%
1100 1579
 
4.5%
1200 1355
 
3.9%
Other values (105) 13772
39.3%
ValueCountFrequency (%)
100 305
 
0.9%
200 1312
3.7%
300 2615
7.5%
400 2478
7.1%
500 2208
6.3%
600 2155
6.1%
700 2198
6.3%
800 2003
5.7%
900 1757
5.0%
1000 1738
5.0%
ValueCountFrequency (%)
10000 6
< 0.1%
9900 1
 
< 0.1%
9800 5
< 0.1%
9700 3
< 0.1%
9600 1
 
< 0.1%
9500 5
< 0.1%
9400 3
< 0.1%
9300 5
< 0.1%
9200 2
 
< 0.1%
9100 1
 
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct732
Distinct (%)2.1%
Missing506
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean58.534682
Minimum0.2142
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:17.865864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q110
median45
Q384
95-th percentile185
Maximum390
Range389.7858
Interquartile range (IQR)74

Descriptive statistics

Standard deviation58.401448
Coefficient of variation (CV)0.99772384
Kurtosis1.9634402
Mean58.534682
Median Absolute Deviation (MAD)36
Skewness1.4209395
Sum2022841.6
Variance3410.7292
MonotonicityNot monotonic
2024-03-08T12:14:18.070598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3148
 
9.0%
4 872
 
2.5%
3 864
 
2.5%
5 655
 
1.9%
6 582
 
1.7%
8 509
 
1.5%
7 491
 
1.4%
10 457
 
1.3%
9 423
 
1.2%
1 387
 
1.1%
Other values (722) 26170
74.6%
(Missing) 506
 
1.4%
ValueCountFrequency (%)
0.2142 4
 
< 0.1%
0.4284 7
 
< 0.1%
0.6426 10
 
< 0.1%
0.8568 9
 
< 0.1%
1 387
1.1%
1.071 7
 
< 0.1%
1.2852 13
 
< 0.1%
1.4994 16
 
< 0.1%
1.7136 9
 
< 0.1%
1.9278 15
 
< 0.1%
ValueCountFrequency (%)
390 1
< 0.1%
367 1
< 0.1%
364 1
< 0.1%
361 1
< 0.1%
351 1
< 0.1%
349 1
< 0.1%
348 1
< 0.1%
347 1
< 0.1%
334 1
< 0.1%
333 2
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct963
Distinct (%)2.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.67149
Minimum-16.8
Maximum41.1
Zeros332
Zeros (%)0.9%
Negative5222
Negative (%)14.9%
Memory size274.1 KiB
2024-03-08T12:14:18.268253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4
Q13.1
median14.6
Q323.5
95-th percentile30.7
Maximum41.1
Range57.9
Interquartile range (IQR)20.4

Descriptive statistics

Standard deviation11.458418
Coefficient of variation (CV)0.83812507
Kurtosis-1.1703241
Mean13.67149
Median Absolute Deviation (MAD)9.9
Skewness-0.10086633
Sum479103.68
Variance131.29535
MonotonicityNot monotonic
2024-03-08T12:14:18.452468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 401
 
1.1%
0 332
 
0.9%
1 326
 
0.9%
-1 315
 
0.9%
2 302
 
0.9%
-2 240
 
0.7%
-4 212
 
0.6%
4 197
 
0.6%
5 196
 
0.6%
-5 193
 
0.6%
Other values (953) 32330
92.2%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
41.1 1
< 0.1%
40.4 1
< 0.1%
40 1
< 0.1%
39.6 1
< 0.1%
38.8 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38.1 1
< 0.1%
38 2
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct595
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1012.5474
Minimum987.1
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:18.649438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum987.1
5-th percentile997
Q11004
median1012.2
Q31020.9
95-th percentile1029.2
Maximum1042
Range54.9
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation10.266059
Coefficient of variation (CV)0.010138843
Kurtosis-0.9080139
Mean1012.5474
Median Absolute Deviation (MAD)8.4
Skewness0.09963305
Sum35483712
Variance105.39196
MonotonicityNot monotonic
2024-03-08T12:14:18.911251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 305
 
0.9%
1021 258
 
0.7%
1025 254
 
0.7%
1024 253
 
0.7%
1022 244
 
0.7%
1020 240
 
0.7%
1026 230
 
0.7%
1014 223
 
0.6%
1019 222
 
0.6%
1016 221
 
0.6%
Other values (585) 32594
93.0%
ValueCountFrequency (%)
987.1 1
 
< 0.1%
987.5 1
 
< 0.1%
987.7 3
< 0.1%
987.8 3
< 0.1%
987.9 1
 
< 0.1%
988.1 1
 
< 0.1%
988.4 1
 
< 0.1%
988.5 1
 
< 0.1%
988.6 2
< 0.1%
988.8 1
 
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)1.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.4475345
Minimum-35.3
Maximum28.8
Zeros74
Zeros (%)0.2%
Negative15475
Negative (%)44.1%
Memory size274.1 KiB
2024-03-08T12:14:19.139559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-20.2
Q1-8.8
median3
Q315
95-th percentile22.1
Maximum28.8
Range64.1
Interquartile range (IQR)23.8

Descriptive statistics

Standard deviation13.810696
Coefficient of variation (CV)5.6426971
Kurtosis-1.1106335
Mean2.4475345
Median Absolute Deviation (MAD)11.9
Skewness-0.19689612
Sum85771.4
Variance190.73532
MonotonicityNot monotonic
2024-03-08T12:14:19.394117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 142
 
0.4%
16.9 133
 
0.4%
16.4 130
 
0.4%
16.2 130
 
0.4%
17.2 129
 
0.4%
17 129
 
0.4%
17.8 128
 
0.4%
17.1 128
 
0.4%
17.3 126
 
0.4%
17.7 122
 
0.3%
Other values (607) 33747
96.2%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.8 2
< 0.1%
28.7 3
< 0.1%
28.5 2
< 0.1%
28.4 4
< 0.1%
28.3 2
< 0.1%
28.2 3
< 0.1%
28.1 3
< 0.1%
28 1
 
< 0.1%
27.9 1
 
< 0.1%
27.8 4
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct119
Distinct (%)0.3%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.064019518
Minimum0
Maximum46.4
Zeros33673
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:19.598148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46.4
Range46.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78628199
Coefficient of variation (CV)12.28191
Kurtosis1020.5059
Mean0.064019518
Median Absolute Deviation (MAD)0
Skewness27.335814
Sum2243.5
Variance0.61823937
MonotonicityNot monotonic
2024-03-08T12:14:19.830072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.5 74
 
0.2%
0.4 71
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.9 41
 
0.1%
0.8 35
 
0.1%
Other values (109) 471
 
1.3%
ValueCountFrequency (%)
0 33673
96.0%
0.1 310
 
0.9%
0.2 147
 
0.4%
0.3 118
 
0.3%
0.4 71
 
0.2%
0.5 74
 
0.2%
0.6 57
 
0.2%
0.7 47
 
0.1%
0.8 35
 
0.1%
0.9 41
 
0.1%
ValueCountFrequency (%)
46.4 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
29 1
< 0.1%
27.3 1
< 0.1%
24.1 1
< 0.1%
23.7 1
< 0.1%
21.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing78
Missing (%)0.2%
Memory size274.1 KiB
ENE
3861 
E
3564 
NE
3540 
ESE
2706 
SW
2481 
Other values (11)
18834 

Length

Max length3
Median length2
Mean length2.2486423
Min length1

Characters and Unicode

Total characters78671
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowNW
3rd rowNNW
4th rowN
5th rowNNW

Common Values

ValueCountFrequency (%)
ENE 3861
11.0%
E 3564
10.2%
NE 3540
10.1%
ESE 2706
 
7.7%
SW 2481
 
7.1%
NW 2466
 
7.0%
SSW 1953
 
5.6%
NNE 1928
 
5.5%
SE 1880
 
5.4%
N 1865
 
5.3%
Other values (6) 8742
24.9%

Length

2024-03-08T12:14:20.062231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ene 3861
11.0%
e 3564
10.2%
ne 3540
10.1%
ese 2706
 
7.7%
sw 2481
 
7.1%
nw 2466
 
7.0%
ssw 1953
 
5.6%
nne 1928
 
5.5%
se 1880
 
5.4%
n 1865
 
5.3%
Other values (6) 8742
25.0%

Most occurring characters

ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 78671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 25448
32.3%
N 20321
25.8%
S 17093
21.7%
W 15809
20.1%

WSPM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.8607846
Minimum0
Maximum10.5
Zeros623
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:14:20.282316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11
median1.5
Q32.4
95-th percentile4.455
Maximum10.5
Range10.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2803683
Coefficient of variation (CV)0.68807978
Kurtosis3.3696482
Mean1.8607846
Median Absolute Deviation (MAD)0.6
Skewness1.566528
Sum65220.5
Variance1.6393429
MonotonicityNot monotonic
2024-03-08T12:14:20.510235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1863
 
5.3%
1.1 1838
 
5.2%
1 1782
 
5.1%
1.3 1713
 
4.9%
0.9 1642
 
4.7%
1.4 1572
 
4.5%
1.5 1427
 
4.1%
0.8 1385
 
3.9%
1.6 1324
 
3.8%
0.7 1269
 
3.6%
Other values (91) 19235
54.9%
ValueCountFrequency (%)
0 623
 
1.8%
0.1 234
 
0.7%
0.2 280
 
0.8%
0.3 244
 
0.7%
0.4 473
 
1.3%
0.5 691
2.0%
0.6 956
2.7%
0.7 1269
3.6%
0.8 1385
3.9%
0.9 1642
4.7%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10.3 1
 
< 0.1%
10.2 1
 
< 0.1%
9.9 2
< 0.1%
9.8 1
 
< 0.1%
9.7 4
< 0.1%
9.6 1
 
< 0.1%
9.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Nongzhanguan
35064 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters420768
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNongzhanguan
2nd rowNongzhanguan
3rd rowNongzhanguan
4th rowNongzhanguan
5th rowNongzhanguan

Common Values

ValueCountFrequency (%)
Nongzhanguan 35064
100.0%

Length

2024-03-08T12:14:20.716864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:14:20.889439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
nongzhanguan 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
n 105192
25.0%
g 70128
16.7%
a 70128
16.7%
N 35064
 
8.3%
o 35064
 
8.3%
z 35064
 
8.3%
h 35064
 
8.3%
u 35064
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 385704
91.7%
Uppercase Letter 35064
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 105192
27.3%
g 70128
18.2%
a 70128
18.2%
o 35064
 
9.1%
z 35064
 
9.1%
h 35064
 
9.1%
u 35064
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 420768
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 105192
25.0%
g 70128
16.7%
a 70128
16.7%
N 35064
 
8.3%
o 35064
 
8.3%
z 35064
 
8.3%
h 35064
 
8.3%
u 35064
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 105192
25.0%
g 70128
16.7%
a 70128
16.7%
N 35064
 
8.3%
o 35064
 
8.3%
z 35064
 
8.3%
h 35064
 
8.3%
u 35064
 
8.3%

Interactions

2024-03-08T12:14:07.915754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:30.631673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:33.139762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.740747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.327430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.752990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.206497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.747447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:48.531320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.855606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.375935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:57.243083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.738305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.357879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.069516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.067515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:30.813981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:33.312153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.935109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.479134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.983085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.453769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.917923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:48.777577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.052814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.569630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:57.502879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.179619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.499165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.299812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.279666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:30.958531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:33.467571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.069243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.661046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.168824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.609902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.076268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:49.012223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.223488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.708137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:57.695747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.290369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.661859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.477188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.449621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.119791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:33.640479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.300644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.805906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.335430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.740284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.233101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:49.189806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.394026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.877548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:57.833610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.442298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.798204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.649545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.709083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.255297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:33.857314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.472556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.913574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.470026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.891324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.396018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:49.375239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.584126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.070800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:57.968853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.551345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.919509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.815337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.866803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.453864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.051186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.615418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:39.087341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.597625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.103691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.574862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:49.603075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.762825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.231987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:58.105673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.675421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:03.043670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:05.958676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:08.997199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.609758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.175359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.769603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:39.249955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.755267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.324178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.771099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:49.795210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:52.932527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.464236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:58.316020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.821300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:03.199840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:06.145321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:09.141541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.762548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.337591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:36.923665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:39.410427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:41.890843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.475863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:46.962853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:50.551332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.084933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.622319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:58.529432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:00.994116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:03.339870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:06.313936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:09.328857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:31.931894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.532188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:37.075590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:39.548651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.067020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.657770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:47.101644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:50.725270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.213419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.784109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:58.724049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:01.152451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:03.575808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:06.539201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:09.502446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.113913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.721355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:37.240618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:39.948490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.209913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.792820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:47.261336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:50.878689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.358149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:55.963376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:58.878308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:01.287761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:03.770466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:06.697686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:09.677688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.297228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:34.890761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:37.466864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.101871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.409191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:44.933391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:47.492660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.063011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.549680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:56.190043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.058078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:01.540523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:04.016997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:06.946559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:09.884480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.481782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.047827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:37.661909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.279767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.588512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.116556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:47.769093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.242458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.743160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:56.378593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.181490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:01.733329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:04.241720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:07.126190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:10.599555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.638695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.243162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:37.842979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.387686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.767167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.294962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:48.008673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.388187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:53.923926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:56.531088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.300342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:01.881383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:04.465708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:07.322335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:10.783817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.835631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.421580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.035185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.512477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:42.946331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.449062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:48.262346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.566416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.133549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:56.728238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.478505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.041959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:04.716341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:07.481319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:10.971218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:32.977977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:35.582813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:38.181818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:40.631992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:43.075666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:45.581536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:48.400196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:51.727211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:54.255410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:56.955909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:59.591842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:02.212740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:04.887108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:07.684260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:14:21.058335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1650.748-0.049-0.4550.7570.8640.0450.0140.617-0.188-0.461-0.000-0.0660.0770.1250.076
DEWP0.1651.000-0.010-0.0890.2330.1390.233-0.7750.178-0.3260.815-0.2090.019-0.0210.2560.1220.148
NO20.748-0.0101.000-0.067-0.6650.6570.6730.138-0.0820.540-0.300-0.5400.028-0.0870.0560.1310.066
No-0.049-0.089-0.0671.000-0.026-0.080-0.0610.1670.000-0.233-0.1150.0130.0180.0010.0440.0850.862
O3-0.4550.233-0.665-0.0261.000-0.248-0.283-0.426-0.004-0.2450.5770.468-0.0110.301-0.1700.1620.063
PM100.7570.1390.657-0.080-0.2481.0000.897-0.078-0.0910.576-0.048-0.2810.0300.019-0.0130.0940.072
PM2.50.8640.2330.673-0.061-0.2830.8971.000-0.074-0.0280.586-0.051-0.3660.012-0.0210.0150.0960.057
PRES0.045-0.7750.1380.167-0.426-0.078-0.0741.000-0.0870.299-0.8410.0000.010-0.037-0.0110.0790.148
RAIN0.0140.178-0.0820.000-0.004-0.091-0.028-0.0871.000-0.1680.039-0.005-0.010-0.0070.0420.0050.010
SO20.617-0.3260.540-0.233-0.2450.5760.5860.299-0.1681.000-0.393-0.1440.0140.007-0.1820.0620.104
TEMP-0.1880.815-0.300-0.1150.577-0.048-0.051-0.8410.039-0.3931.0000.1300.0180.1460.1260.1100.148
WSPM-0.461-0.209-0.5400.0130.468-0.281-0.3660.000-0.005-0.1440.1301.0000.0030.181-0.1520.1810.042
day-0.0000.0190.0280.018-0.0110.0300.0120.010-0.0100.0140.0180.0031.0000.0000.0100.0310.000
hour-0.066-0.021-0.0870.0010.3010.019-0.021-0.037-0.0070.0070.1460.1810.0001.0000.0000.1280.000
month0.0770.2560.0560.044-0.170-0.0130.015-0.0110.042-0.1820.126-0.1520.0100.0001.0000.0850.249
wd0.1250.1220.1310.0850.1620.0940.0960.0790.0050.0620.1100.1810.0310.1280.0851.0000.089
year0.0760.1480.0660.8620.0630.0720.0570.1480.0100.1040.1480.0420.0000.0000.2490.0891.000

Missing values

2024-03-08T12:14:11.317831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:14:11.798104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:14:12.272620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133105.014.04.012.0200.085.0-0.51024.5-21.40.0NNW5.7Nongzhanguan
1220133118.012.06.014.0200.084.0-0.71025.1-22.10.0NW3.9Nongzhanguan
2320133123.06.05.014.0200.083.0-1.21025.3-24.60.0NNW5.3Nongzhanguan
3420133135.05.05.014.0200.084.0-1.41026.2-25.50.0N4.9Nongzhanguan
4520133145.05.06.021.0200.077.0-1.91027.1-24.50.0NNW3.2Nongzhanguan
5620133153.03.013.021.0300.077.0-2.41027.5-21.30.0NW2.4Nongzhanguan
6720133164.04.015.032.0300.062.0-2.51028.2-20.40.0NW2.2Nongzhanguan
7820133173.07.014.045.0400.048.0-1.41029.5-20.40.0NNW3.0Nongzhanguan
8920133183.06.013.049.0400.046.0-0.31030.4-21.20.0NW4.6Nongzhanguan
910201331911.014.09.028.0400.068.00.41030.5-23.30.0N5.5Nongzhanguan
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
3505435055201722814NaN6.02.010.0200.094.014.61013.3-15.60.0N3.6Nongzhanguan
3505535056201722815NaN8.03.011.0200.091.015.41013.0-15.00.0NNW3.3Nongzhanguan
35056350572017228163.016.04.011.0300.092.014.91012.6-15.40.0NW2.1Nongzhanguan
350573505820172281712.040.05.016.0300.087.014.21012.5-14.90.0NW3.1Nongzhanguan
350583505920172281816.023.03.019.0300.082.013.41013.0-15.50.0WNW1.4Nongzhanguan
350593506020172281914.021.03.027.0400.072.012.51013.5-16.20.0NW2.4Nongzhanguan
350603506120172282018.027.03.037.0400.059.011.61013.6-15.10.0WNW0.9Nongzhanguan
350613506220172282115.039.05.050.0600.041.010.81014.2-13.30.0NW1.1Nongzhanguan
350623506320172282211.035.06.049.0500.041.010.51014.4-12.90.0NNW1.2Nongzhanguan
350633506420172282310.028.07.048.0600.039.08.61014.1-15.90.0NNE1.3Nongzhanguan